Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites....
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Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, therefore, is essential for preventing network security intrusions and system attacks. Many AI-based anomaly detection methods have been proposed and achieved high detection performance, however, are still a ”black box” that is hard to be interpreted. In this study, we suggest using Explainable Artificial Intelligence to enhance the perspective and reliable results of an lstm-based autoencoder-OCSVM learning model for anomaly detection in ICS. We demonstrate the performance of our proposed method based on a well-known SCADA dataset.
Financial time-series forecasting, and profit maximization is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep lear...
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Financial time-series forecasting, and profit maximization is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep learning system, which uses a variety of data for a subset of the stocks on the NASDAQ exchange to forecast the stock price. Our framework allows the use of a variational autoencoder (VAE) to remove noise and time-series data engineering to extract higher-level features. A Stacked lstm autoencoder is used to perform multi-step-ahead prediction of the stock closing price. This prediction is used by two profit-maximization strategies that include greedy approach and short selling. Besides, we use reinforcement learning as a third profit-enhancement strategy and compare these three strategies to offline strategies that use the actual future prices. Results show that the proposed methods outperform the state-of-the-art time-series forecasting approaches in terms of predictive accuracy and profitability.
Anomaly detection is an active research field which attracts the attention of many business and research actors. It has led to several research projects depending on the nature of the data, the availability of labels ...
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ISBN:
(纸本)9781728169262
Anomaly detection is an active research field which attracts the attention of many business and research actors. It has led to several research projects depending on the nature of the data, the availability of labels on normality, and domains of application that are diverse such as fraud detection, medical domains, cloud monitoring or network intrusions detection, etc. However, dealing with effective anomaly detection for complex and high-dimensional time series data remains a challenging task. In this work, we propose hybrid approach composed of an lstm autoencoder trained on normal records to learn efficient normal sequence representations combined with an SVM classifier for anomaly detection. Experimental results show that by encoding time series via a pretrained lstm encoder allows efficient representation of data so that we can accurately detect abnormal records. In fact, the encoded representation reduces significantly the correlations between normal and abnormal records and allows us to have an efficient latent data representation that separates consistently the two classes. The proposed hybrid approach outperforms state-of-the art approaches [1], [2], [3], [4].
Operation mode recognition is a prerequisite for precise regulation of key performance indicators (KPIs) in industrial processes. However, system uncertainties and the complexity of the process dynamics pose significa...
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Operation mode recognition is a prerequisite for precise regulation of key performance indicators (KPIs) in industrial processes. However, system uncertainties and the complexity of the process dynamics pose significant challenges in achieving accurate mode partitioning. This study proposes a control-oriented operation mode recognition method called attention-long short-term memory-Monte Carlo simulation (AT-lstm-MC). First, a fuzzy inference-based ‘indicator regulation potential’ evaluation framework is established to quantitatively describe the maximum control potential of each control variable on KPIs. Subsequently, considering the temporal dependencies of industrial process data, a long short-term memory (lstm) autoencoder network is employed as the core architecture for feature extraction, where the ‘indicator regulation potential’ guides the lstm autoencoder through attention layers to extract control-oriented deep clustering features. Finally, the K-means clustering method is utilized to determine the system operation modes based on these deep clustering features. To address uncertainty-induced challenges, multiple Monte Carlo simulations are performed on the operation mode recognition for the same period, thereby obtaining a statistically convergent operation mode. The effectiveness of the proposed method is validated through a case study of an actual industrial process.
Assessing construction productivity objectively and in real-time remains challenging. This study proposes an automated framework leveraging Building Information Modeling (BIM) interaction logs and predictive modeling....
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Assessing construction productivity objectively and in real-time remains challenging. This study proposes an automated framework leveraging Building Information Modeling (BIM) interaction logs and predictive modeling. The methodology involves systematic log data processing and feature engineering, generation of pseudo-label productivity scores derived from an initial expert-informed model, and a rigorous comparative evaluation of predictive architectures. Sixteen diverse models were evaluated using 10-fold cross-validation on a 500-instance dataset derived from construction logs. The cross-validation identified XGBoost as the top-performing architecture (R2 = 0.97 +/- 0.01), demonstrating the effectiveness of gradient boosting on the engineered tabular features. The framework incorporates an integrated interface with visualization and natural language processing for enhanced insight generation and accessibility. While acknowledging limitations concerning pseudo-label usage and initial data processing steps, this research presents a robust, validated methodology for data-driven productivity assessment, offering a scalable alternative to traditional methods in construction project management.
Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning ne...
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Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, public safety. In this paper, we propose a comprehensive framework for detecting network anomalies using mobile traffic data: collecting data from the LTE Physical Downlink Control Channel (PDCCH) of different eNodeBs, we implement deep learning algorithms in a semi-supervised way to detect potential traffic anomalies that are generated, for example, by unexpected crowd gathering. With respect to other types of mobile dataset, using LTE PDCCH information, we are able to obtain fine-grained and high-resolution data for the users that are connected to the LTE eNodeB. Through a semi-supervised approach, algorithms are trained to detect anomalies using only one class of traffic samples. We design two algorithms based on stacked-lstm Neural Networks: 1) lstm autoencoder (lstm-AE), in which the objective is to reconstruct the traffic samples 2) lstm traffic predictor (lstm-PRED), where the goal is to predict the traffic in the next time-instants, based on historical data. In both cases, we analyze the reconstruction (or prediction) error to assess if the mobile traffic presents anomalies or not. Using the F1-score as metric, we demonstrate that the proposed methods are able to identify the anomalous traffic periods, beating a benchmark that comprises different state-of-the-art algorithms for anomaly detection.
Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. Current data analytics approaches show decent results concerning the optimization ...
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Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. Current data analytics approaches show decent results concerning the optimization of single processes but lack in extensibility to plants with high-dimensional data spaces. This paper presents and compares two data-driven self-learning approaches that are used to detect anomalies within large amounts of machine and process data. Models of the machine behavior are generated to capture complex interdependencies and to extract features that represent anomalies. The approaches are tested and evaluated on the basis of real industrial data from metal forming processes. (C) 2019 The Authors. Published by Elsevier B.V.
Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. Current data analytics approaches show decent results concerning the optimization ...
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Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. Current data analytics approaches show decent results concerning the optimization of single processes but lack in extensibility to plants with high-dimensional data spaces. This paper presents and compares two data-driven self-learning approaches that are used to detect anomalies within large amounts of machine and process data. Models of the machine behavior are generated to capture complex interdependencies and to extract features that represent anomalies. The approaches are tested and evaluated on the basis of real industrial data from metal forming processes.
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